MaskedArray.squeeze(axis=None) [source] Remove single-dimensional entries from the shape of a. Refer to numpy.squeeze for full documentation. See also numpy.squeeze equivalent function

ndarray.ptp(axis=None, out=None) Peak to peak (maximum - minimum) value along a given axis. Refer to numpy.ptp for full documentation. See also numpy.ptp equivalent function

MaskedArray.argsort(axis=None, kind='quicksort', order=None, fill_value=None) [source] Return an ndarray of indices that sort the array along the specified axis. Masked values are filled beforehand to fill_value. Parameters: axis : int, optional Axis along which to sort. The default is -1 (last axis). If None, the flattened array is used. fill_value : var, optional Value used to fill the array before sorting. The default is the fill_value attribute of the input array. kind : {?quicksor

numpy.polynomial.hermite.hermsub(c1, c2) [source] Subtract one Hermite series from another. Returns the difference of two Hermite series c1 - c2. The sequences of coefficients are from lowest order term to highest, i.e., [1,2,3] represents the series P_0 + 2*P_1 + 3*P_2. Parameters: c1, c2 : array_like 1-D arrays of Hermite series coefficients ordered from low to high. Returns: out : ndarray Of Hermite series coefficients representing their difference. See also hermadd, hermmul, he

numpy.diag(v, k=0) [source] Extract a diagonal or construct a diagonal array. See the more detailed documentation for numpy.diagonal if you use this function to extract a diagonal and wish to write to the resulting array; whether it returns a copy or a view depends on what version of numpy you are using. Parameters: v : array_like If v is a 2-D array, return a copy of its k-th diagonal. If v is a 1-D array, return a 2-D array with v on the k-th diagonal. k : int, optional Diagonal in qu

numpy.ma.cumprod(self, axis=None, dtype=None, out=None) = Return the cumulative product of the elements along the given axis. The cumulative product is taken over the flattened array by default, otherwise over the specified axis. Masked values are set to 1 internally during the computation. However, their position is saved, and the result will be masked at the same locations. Parameters: axis : {None, -1, int}, optional Axis along which the product is computed. The default (axis = None)

MaskedArray.product(axis=None, dtype=None, out=None) [source] Return the product of the array elements over the given axis. Masked elements are set to 1 internally for computation. Parameters: axis : {None, int}, optional Axis over which the product is taken. If None is used, then the product is over all the array elements. dtype : {None, dtype}, optional Determines the type of the returned array and of the accumulator where the elements are multiplied. If dtype has the value None and t

HermiteE.has_samewindow(other) [source] Check if windows match. New in version 1.6.0. Parameters: other : class instance The other class must have the window attribute. Returns: bool : boolean True if the windows are the same, False otherwise.

ndarray.flat A 1-D iterator over the array. This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python?s built-in iterator object. See also flatten Return a copy of the array collapsed into one dimension. flatiter Examples >>> x = np.arange(1, 7).reshape(2, 3) >>> x array([[1, 2, 3], [4, 5, 6]]) >>> x.flat[3] 4 >>> x.T array([[1, 4], [2, 5], [3, 6]]) >>> x.T.flat[3] 5 >>> type(x

numpy.ma.atleast_2d(*arys) = View inputs as arrays with at least two dimensions. Parameters: arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have two or more dimensions are preserved. Returns: res, res2, ... : ndarray An array, or tuple of arrays, each with a.ndim >= 2. Copies are avoided where possible, and views with two or more dimensions are returned. Notes The function is applied to both the _dat